Fairness in ranking and recommendation systems is an active area of research. Since recommender systems involve multiple stakeholders [1]}, [2]}, fairness has been considered from the perspective of both users and item producers. On the user side, a common goal is to prevent disparities in recommendation performance across sensitive groups of users [3]}, [4]}. On the item side, authors aim to prevent winner-take-all effects [5]} by redistributing exposure across groups of producers, either towards equal exposure, or equal ratios of exposure to relevance [6]}, [7]}, [8]}, [9]}, sometimes measured by the classical Gini index [10]}, [11]}.
